On the Optimal Deployment of Power Beacons for Massive Wireless Energy Transfer

被引:15
作者
Rosabal, Osmel Martinez [1 ]
Lopez, Onel L. Alcaraz [1 ]
Alves, Hirley [1 ]
Montejo-Sanchez, Samuel [2 ]
Latva-Aho, Matti [1 ]
机构
[1] Univ Oulu, Ctr Wireless Commun, Oulu 90570, Finland
[2] Univ Tecnol Metropolitana, Programa Inst Fomento I D I, Santiago 8940577, Chile
基金
芬兰科学院;
关键词
Performance evaluation; Wireless communication; Wireless sensor networks; Energy exchange; Lead; Benchmark testing; Approximation algorithms; Deployment optimization; massive Internet of Things (IoT); power beacons (PBs); wireless energy transfer (WET); NETWORKS; SYSTEMS; OPTIMIZATION; EFFICIENCY; PLACEMENT; INTERNET; DESIGN;
D O I
10.1109/JIOT.2020.3048065
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wireless energy transfer (WET) is emerging as an enabling green technology for Internet-of-Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio-frequency transmitters called power beacons (PBs). In this article, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.
引用
收藏
页码:10531 / 10542
页数:12
相关论文
共 42 条
[1]  
Al-Sakkaf A, 2019, P ANN C CANADIAN SOC, P1
[2]   Machine Learning for Wireless Communication Channel Modeling: An Overview [J].
Aldossari, Saud Mobark ;
Chen, Kwang-Cheng .
WIRELESS PERSONAL COMMUNICATIONS, 2019, 106 (01) :41-70
[3]   Mixed-Integer Programming based Techniques for Resource Allocation in Underlay Cognitive Radio Networks: A Survey [J].
Alfa, Attahiru S. ;
Maharaj, B. T. ;
Lall, Shruti ;
Pal, Sougata .
JOURNAL OF COMMUNICATIONS AND NETWORKS, 2016, 18 (05) :744-761
[4]   Beamforming in Wireless Energy Harvesting Communications Systems: A Survey [J].
Alsaba, Yamen ;
Kamal, Sharul ;
Rahim, Abdul ;
Leow, Chee Yen .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (02) :1329-1360
[5]  
[Anonymous], 2016, HDB STAT DISTRIBUTIO
[6]  
[Anonymous], 2005, WIRELESS COMMUNICATI
[7]  
[Anonymous], 2013, Mach. Learn. Rep.
[8]  
[Anonymous], 2010, 44 CISS
[9]   Optimal Placement of Wireless Chargers in Rechargeable Sensor Networks [J].
Arivudainambi, D. ;
Balaji, S. .
IEEE SENSORS JOURNAL, 2018, 18 (10) :4212-4222
[10]   Placement Optimization of Energy and Information Access Points in Wireless Powered Communication Networks [J].
Bi, Suzhi ;
Zhang, Rui .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2016, 15 (03) :2351-2364